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The mounting behaviour of dairy cows is driven by oestrus and is also related to their welfare. To improve the accuracy and speed of the detection of mounting of dairy cows in dense scenes, a detection model for the mounting behaviour of dairy cows was developed in this study based on the feature enhancement module. Firstly, an improved attention module (C3GC-3) based on global context information and convolution was proposed to capture long-distance dependence. Then, a feature enhancement module based on atrous spatial pyramid pooling and the C3GC-3 was designed to realise the multiscale fusion of high-level semantic information and improve the feature extraction ability of the model for dense dairy cows images. Finally, the K-means clustering algorithm was used to obtain new anchors for the mounting behaviour of dairy cows, and CIoU was used to optimise the loss function. A camera was installed on a farm containing 200 dairy cows for data collection. The mosaic method was used to expand the 2668 images of the mounting behaviour of dairy cows to train the model, and the remaining 675 images were used to test the model. The experimental results showed that the proposed model had a high inference speed of 61 fps, and the detection time for each image was 16.3 ms, which met the real-time performance requirements for detecting mounting in dairy cows. The mean average precision of the model was 94.3%, which was 5.9% higher than that of YOLOv5l. The proposed model showed promising results regarding the mounting of dairy cows and might be used in any weather. • A mounting behaviour dataset of group-raised dairy cows in dense scenes was proposed.. • ASPP can improve the accuracy of multi-scale object detection. • Attention mechanism can improve the feature extraction ability of the model. • Mean average precision model was 94.3%, which is 5.9% higher than that of YOLOv5l. [ABSTRACT FROM AUTHOR]